frequency of pairs of non-missing data in experiments 1, 2, & 3

This does the Galton dataset with instructions for jperforming the analsyes in Jamovi

Short R program to accompany Chapter 11 of "Understanding Structural Equation Models by Phillip K. Wood

Program Code to Accompany Chapter 3 of Understanding Structural Equation Models by Phillip K. Wood

Programs to Accompany "Understanding Structural Equation Models" by Phillip K. Wood

Analyses for sideways request- growth model is at the bottom.

Programming to accompany Chapter 17 of Understanding Structural Equation Models by Phillip K. Wood

Exploratory Factor Rotation

Analyses to accompany Chapter 15 of Understanding Structural Equation Models by Phillip K. Wood

Program Code to accompany Chapter 14 of Understanding Structural Equation Modeling by Phil Wood

Figures and Program Code to Accompany Chapter 13 of "Understanding Structural Equation Modeling" by Phil Wood

Program Code to Accompany "Understanding Structural Equation Models" by Phillip K. Wood

R Code to Accompany Chapter 10 of Wood Understanding Structural Equation Modeling. Psychometric Measurement Models

R Code to accompany figures and data examples in Understanding Structural Equation Models by Phillip K. Wood

Figures to accompany Chapter 7 of Wood Understanding Structural Equation Models

Figures to Accompany Chapter 6 of Wood "Understanding Structural Equation Models"

Chapter 5 discusses regression diagnostics, how they provide information about the reasonableness of model assumptions and possible remedies.

Markdown to accompany Chapter 3 of Wood Understanding Structural Equation Modeling

Function to generate a simulated data set in which means, standard deviations and correlations match exactly user-specified values. Useful for doing example analyses in which numbers turn out "nice." Not to be used to explore sampling behavior in research.

This are the corresponding data sets and r-code to accompany Chapter 4 of Understanding Structural Equation Models